Estimation of Corporate Greenhouse Gas Emissions via Machine Learning
This addresses the need for reliable emissions data for investors to comply with climate regulations, but it is incremental as it applies existing machine learning methods to a new domain-specific dataset.
The paper tackles the problem of estimating corporate greenhouse gas emissions for companies that do not disclose them, using a machine learning model trained on disclosed data to provide accurate estimates, enabling investors to align investments with regulatory measures and net-zero goals.
As an important step to fulfill the Paris Agreement and achieve net-zero emissions by 2050, the European Commission adopted the most ambitious package of climate impact measures in April 2021 to improve the flow of capital towards sustainable activities. For these and other international measures to be successful, reliable data is key. The ability to see the carbon footprint of companies around the world will be critical for investors to comply with the measures. However, with only a small portion of companies volunteering to disclose their greenhouse gas (GHG) emissions, it is nearly impossible for investors to align their investment strategies with the measures. By training a machine learning model on disclosed GHG emissions, we are able to estimate the emissions of other companies globally who do not disclose their emissions. In this paper, we show that our model provides accurate estimates of corporate GHG emissions to investors such that they are able to align their investments with the regulatory measures and achieve net-zero goals.